Estimating the impact of COVID-19 on air travel in the medium and long term using neural network and Monte Carlo simulation

The COVID-19 pandemic has had a substantial impact on the airline industry. Air travel in the United States declined in 2020 with significantly lower domestic and international flights. The dynamic change and uncertainty in the trend of COVID-19 have made it difficult to predict future air travel. This paper aims at developing and testing neural network models that predict domestic and international air travel in the medium and long term based on residents' daily trips by distance, economic condition, COVID-19 severity, and travel restrictions. Data in the United States from various sources were used to train and validate the neural network models, and Monte Carlo simulations were constructed to predict air travel under uncertainty of the pandemic and economic growth. The results show that weekly economic index (WEI) is the most important predictor for air travel. Additionally, daily trips by distance play a more important role in the prediction of domestic air travel than the international one, while travel restrictions seem to have an impact on both. Sensitivity analysis results for four different scenarios indicate that air travel in the future is more sensitive to the change in WEI than the changes in COVID-19 variables. Additionally, even in the best-case scenario, when the pandemic is over and the economy is back to normal, it still takes several years for air travel to return to normal, as before the pandemic. The findings have significant contributions to the literature in COVID-19's impact on air transportation and air travel prediction.

Projecting daily travel behavior by distance during the pandemic and the spread of COVID-19 infections - Are we in a closed loop scenario?

Understanding the future development of COVID-19 is the key to contain the spreading of the coronavirus. The purpose of this paper is to explore a potential relationship between United States residents' daily trips by distance and the COVID-19 infections in the near future. The study used the daily travel data from the Bureau of Transportation Statistics (BTS) and the COVID-19 data from the Centers for Disease Control and Prevention (CDC) in the United States. Time-series forecast models using Autoregressive Moving Average (ARIMA) method were constructed to project future trends of United States residents' daily trips by distance at the national level from November 30, 2020, to February 28, 2021. A comparative trend analysis was conducted to detect the patterns of daily trips and the spread of COVID-19 during that period. The results revealed a closed loop scenario, in which the residents' travel behavior dynamically changes based on their risk perception of COVID-19 in an infinite loop. A detected lag in the travel behavior between short trips and long trips further worsens the situation and creates more difficulties in finding an effective solution to break the loop. The study shed new light on efforts to contain and control the spread of the coronavirus. The loop can only be broken with proper and prompt mitigation strategies to reduce the burden on hospitals and healthcare systems and save more lives.

Telework and daily travel: New evidence from Sweden

Telework, long promoted as a way to reduce daily travel and address congestion problems, has been extensively studied in transport research. Empirical consensus has long held that telework reduces overall travel, but several updated studies now suggest the opposite. Meanwhile, telework has steadily increased in many countries, and few studies have examined contexts where regular teleworkers have grown to form an early majority. We study how telework influences daily travel in such a context, namely, Sweden from 2011 to 2016. Using representative micro-data from the Swedish National Travel Survey, this study also captures travel behaviour during the defined period when the telework was actually practiced, distinguishing different telework arrangements and analysing a range of travel behavioral outcomes. We conclude that telework leads to reduced travel demand, more use of active transport modes, and congestion relief. Important differences between full- and part-day teleworkers are also highlighted, stressing the importance of understanding telework as a diversified coping strategy for organizing the spatiotemporality of everyday life.